Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special.

This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimising

your code.

By reading this book, you will learn:

• The difference between an object and its name, and why the distinction is important




• The important vector data structures, how they fit together, and how you can pull them apart using subsetting




• The fine details of functions and environments




• The condition system, which powers messages, warnings, and errors




• The powerful functional programming paradigm, which can replace many for loops




• The three most important OO systems: S3, S4, and R6




• The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation




• Effective debugging techniques that you can deploy, regardless of how your code is run




• How to find and remove performance bottlenecks

The second edition is a comprehensive update:

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  New foundational chapters: "Names and values," "Control flow," and "Conditions"

  
  




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  comprehensive coverage of object oriented programming with chapters on S3, S4, R6, and how to choose between them

  
  




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  Much deeper coverage of metaprogramming, including the new tidy evaluation framework

  
  




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  use of new package like rlang (http://rlang.r-lib.org), which provides a clean interface to low-level operations, and purr (http://purrr.tidyverse.org/) for functional programming

  
  




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  Use of color in code chunks and figures

  
  

  Hadley Wickham is Chief Scientist at RStudio, an Adjunct Professor at Stanford University and the University of Auckland, and a member of the R Foundation. He is the lead developer of the tidyverse, a collection of R packages, including ggplot2 and dplyr, designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund), R Packages, and ggplot2: Elegant Graphics for Data Analysis.